How the Right Tools Can Improve Big Data Management
Tips for harnessing your information & gaining actionable insights from it
The construction industry has made major improvements in technology adoption over the years. More and more data is being generated each day, but if that data isn’t being used, then what’s the point? It’s hard to manage without the right tools in place. Most companies either simply avoid the issue, or need guidance as they try to sort through their options to properly leverage their data. Getting beyond the big data glut to data-driven decisions requires a four-pronged approach:
Get the right talent, tools and processes in place. Big data presents unique challenges for the construction industry. Understanding which data could be useful—and how this data translates into business intelligence—requires a clear understanding of your organization’s overall goals and vision. With clear direction of how you want your data to work for you, your company can extract meaningful insights. But during this period of transition from data glut to data clarity, it’s important to clearly communicate the time frame and rollout process, so you can more easily manage expectations.
Collect and analyze data with an end goal in mind. Big data can be used to improve performance or processes. But to accomplish these goals, you’ll need personnel who have both worked in the built environment and understand project work but also have critical research and analytical skills. Companies that don’t invest in the right people often experience failures and are slow to realize a return on their investment (ROI). But this important step can help your firm drive performance and generate strategic business insights.
Move beyond siloed data to data integration. Like many other industries, construction is notorious for storing data in silos or scattering it across systems, desktops, phones, tables, hard drives and servers—not to mention cloud locations, other devices and unstructured data like blueprints, timecards, emails and PDFs. Data integration can be a difficult, but not insurmountable, challenge.
Leverage internal or external skills to turn data insights into actionable insights. Data analysis is a highly specialized skill set, and frontline managers and field staff rarely understand how to implement analytical procedures or use analytical tools. Yet, to make your data analysis projects effective, you need cultural buy-in. In an industry where 35% of total costs can be attributed to waste and remedial work, this approach to using big data in the best possible way just makes sense.
Harnessing Big Data Takes the Right Tools
The architecture, engineering and construction (AEC) industry is making huge strides in harnessing big data to improve business outcomes, gain better visibility into operations, and streamline business processes. It’s all done with a little help from better tools and processes, such as:
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Data-driven predictive modeling—Some forward-thinking companies are building real-time systems that enable project owners to visualize—and adjust—project design during the early design stages, to speed up the design process, reduce waste and delay and ensure accurate cost estimates.
Data capture and usage across the business—Rather than have construction teams waste hours looking for project data and information—a problem that’s plagued the industry for years—some leading firms are using prefabrication methods, connected job sites, BIM, virtual reality, wearables, geolocation and sensors to track employees, equipment and material movement, and further optimize tools, resources and worker productivity.
Better collaboration and efficiency—Given increasing AEC project complexity and growing demand for new AEC projects, industry leaders recognize that collaboration is a key part of achieving project consistency and efficiency across multiple stakeholders. Many firms use big data to create automated workflows between stakeholders on a project and keep all relevant personnel informed with relevant real-time updates.
Supplier collaboration platform links trading partners and automates processes—Transactions are executed, and information collected to deliver insights with a speed and accuracy that fuels success on the heavy jobsite. The platform business model can gather data from internet of things (IoT) devices, telematics and more, running analytics to inform about a business’s performance. Critical business processes in a company and across its trading partners are automated, whether the processes are for procurement, production, dispatching, selling, tracking and tracing heavy building materials, and gaining a true picture of job costs.
The digital tools that are most common today support a one-to-many relationship. A collaborative platform supports a many-to-many relationship. When expectations are defined and function requirements and characteristics are specified, productivity can increase, and confusion is eliminated. One platform can standardize exactly how the ordering and purchasing aspect will be performed, helping to understand what jobs are truly costing.
Material costs on a job can make up a large portion of the total construction cost. Connecting with suppliers in real-time enables the capture of those costs, enriches the data for complete two-way communication, and delivers true understanding of what operations are truly costing on a day to day basis. Unlocking visibility made a huge impact for one $1.2 billion project. The results? Millions in project costs savings.
2 Ways to Gain Actionable Insights From Big Data
Good data practices and a solid data strategy will continue to help AEC companies develop and implement sophisticated analytics plans in the years ahead. Companies need to:
Take advantage of business intelligence (BI) tools like dashboards, which store well-defined data in a central location where it can be displayed in a visual format for easier consumption. This enables users to aggregate separate data streams and compare the information within them in a single place and monitor performance in real time.
Include some level of machine learning (ML) and artificial intelligence (AI) to help users gain insight from data and make predictions, discover relationships between data points, identify customer or market segments more intelligently, and identify and learn from patterns hiding in large or unwieldy data sets.
Actionable insights from deep analysis of big data can lead to improved productivity, streamlined operations and lower costs if dealt with properly.
